def plot_cnt_graph(
xvs, # x values
cnt1y, # y values,
cnt2y,
title=None,
label=None,
save_name=None,
y_label=None,
x_label=None):
plt.figure(figsize=(15, 6))
p1 = plt.bar(xvs, cnt1y, 0.35, label="1")
p2 = plt.bar(xvs, cnt2y, 0.35, bottom=cnt1y, label="2")
if title:
plt.title(title) # 'io-static {} performance'.format(label))
if y_label:
plt.ylabel(y_label)
if x_label:
plt.xlabel(x_label)
plt.set_xscale('log')
plt.set_xticks(xvs)
plt.set_xticklabels([i / 1024 for i in xvs], rotation=0)
plt.grid('on', axis='x')
plt.grid('on', axis='y')
plt.legend()
if save_name:
plt.savefig(save_name)
return ax
def plotXPosterior(X, L, Z, space):
"""
Plots X*L(X)/Z in log X space, not including KDE methods
Args:
x : array inputs to likelihood function
L : array likelihood values
Z : float Bayesian evidence
space : string whether to calculate likelihood or log of it
"""
if space == 'log':
LhoodDivZ = np.exp(L - Z)
X = np.exp(X)
else:
LhoodDivZ = L / Z
LXovrZ = X * LhoodDivZ
plt.figure('posterior')
plt.scatter(X, LXovrZ)
plt.set_xscale('log')
plt.show()
plt.close()
def plot_many_curves(data, parameters=None, fname=None):
"""
Plot many curve on the given axis with the given parameters
"""
params = process_parameters(parameters)
plt.rc('font', **params['font'])
fig = plt.figure(num=None, figsize=params['size'], dpi=params['dpi'])
fig.clf()
axis = plt.subplot(111)
for d in data:
plt.plot(d[0], d[1], lw=1, label=d[2])
plt.grid(axis='y', color="0.9", linestyle='-', linewidth=1)
axis.spines['top'].set_visible(False)
axis.spines['right'].set_visible(False)
axis.spines['left'].set_visible(False)
axis.get_xaxis().tick_bottom()
axis.get_yaxis().tick_left()
axis.tick_params(axis='x', direction='out')
axis.tick_params(axis='y', length=0)
for spine in axis.spines.values():
spine.set_position(('outward', 5))
axis.set_axisbelow(True)
axis.set_xlabel(params['xlabel'])
axis.set_ylabel(params['ylabel'])
if params['logscale'] == 'y':
plt.set_yscale('log')
if params['logscale'] == 'x':
plt.set_xscale('log')
plt.legend(loc='upper right', frameon=False)
# draw & show / save image
if fname is None:
plt.draw()
plt.show()
else:
plt.savefig(fname)
def density_profile(sim, linestyle=False, center=True, clear=True, fit=False,in_units=None,
filename=None, fit_factor=0.02, axes=False, **kwargs):
'''
3d density profile
**Options:**
*filename* (None): name of file to which to save output
**Usage:**
>>> import pynbody.plot as pp
>>> h = s.halos()
>>> pp.density_profile(h[1],linestyle='dashed',color='k')
'''
if axes: plt = axes
else: import matplotlib.pyplot as plt
global config
logger.info("Centering...")
if center:
halo.center(sim, mode='ssc')
logger.info("Creating profile...")
if 'min' in kwargs:
ps = profile.Profile(
sim, ndim=3, type='log', nbins=40, min=kwargs['min'])
del kwargs['min']
else:
ps = profile.Profile(sim, ndim=3, type='log', nbins=40)
if clear and not axes:
plt.clf()
critden = (units.Unit('100 km s^-1 Mpc^-1')
* sim.properties['h']) ** 2 / 8.0 / np.pi / units.G
r = ps['rbins'].in_units('kpc')
if in_units is None:
den = ps['density'].in_units(critden)
else:
den = ps['density'].in_units(in_units)
if linestyle:
plt.errorbar(r, den, yerr=den / np.sqrt(ps['n']),
linestyle=linestyle, **kwargs)
else:
plt.errorbar(r, den, yerr=den / np.sqrt(ps['n']),
fmt='o', **kwargs)
if in_units is None:
ylabel=r'$\rho / \rho_{cr}$' # +den.units.latex()+'$]')
else:
ylabel=r'$\rho / '+den.units.latex()+'$'
if axes:
plt.set_yscale('log')
plt.set_xscale('log')
plt.set_xlabel('r [kpc]')
plt.set_ylabel(ylabel) #r'$\rho / \rho_{cr}$') # +den.units.latex()+'$]')
else:
plt.yscale('log')
plt.xscale('log')
plt.xlabel('r [kpc]')
plt.ylabel(ylabel) #r'$\rho / \rho_{cr}$') # +den.units.latex()+'$]')
if (filename):
logger.info("Saving %s", filename)
plt.savefig(filename)
if fit:
fit_inds = np.where(r < fit_factor*sim['r'].max())
alphfit = np.polyfit(np.log10(r[fit_inds]),
np.log10(den[fit_inds]), 1)
# print "alpha: ", alphfit[0], " norm:", alphfit[1]
fit = np.poly1d(alphfit)
plt.plot(r[fit_inds], 10**fit(np.log10(r[fit_inds])),
color='k',linestyle='dashed',
label=r'$\alpha$=%.1f'%alphfit[0])
plt.legend(loc=3)
return alphfit[0]
def plot_shaded_plot(x, y, parameters=None, fname=None):
"""
Plot boxplot for set of data (median and shaded quartiles)
where data is a list of lists of values
data = [ [...] , ... , [...] ]
"""
params = process_parameters(parameters)
plt.rc('font', **params['font'])
perc_05, perc_25, med, perc_75, perc_95 = percentiles(y)
fig = plt.figure(num=None, figsize=params['size'], dpi=params['dpi'])
axis = plt.subplot(111)
plt.clf()
if params['quartiles']:
plt.fill_between(x,
perc_25,
perc_75,
alpha=0.5,
linewidth=0.1,
color=params['quart-color'])
plt.fill_between(x,
perc_75,
perc_95,
alpha=0.25,
linewidth=0.1,
color=params['quart-color'])
plt.fill_between(x,
perc_05,
perc_25,
alpha=0.25,
linewidth=0.1,
color=params['quart-color'])
plt.plot(x,
med,
lw=1,
alpha=1.0,
label=params['label'],
color=params['quart-color'])
plt.grid(axis='y', color="0.9", linestyle='-', linewidth=1)
axis.spines['top'].set_visible(False)
axis.spines['right'].set_visible(False)
axis.spines['left'].set_visible(False)
axis.get_xaxis().tick_bottom()
axis.get_yaxis().tick_left()
axis.tick_params(axis='x', direction='out')
axis.tick_params(axis='y', length=0)
for spine in axis.spines.values():
spine.set_position(('outward', 5))
axis.set_axisbelow(True)
axis.set_xlabel(params['xlabel'])
axis.set_ylabel(params['ylabel'])
if params['logscale'] == 'y':
plt.set_yscale('log')
if params['logscale'] == 'x':
plt.set_xscale('log')
# draw & show / save image
if fname is None:
plt.draw()
plt.show()
else:
plt.savefig(fname)
for h in histograms:
fig += 1
if plotallhistos or h.title.find(opts.SINGLETITLE)>-1:
centers = []
widths = []
sumw = []
sumw2 = []
for bin in h:
centers.append(bin.midpoint)
widths.append(bin.width)
sumw.append(bin.sumW)
sumw2.append(bin.sumW2)
yerr = map(lambda x: math.sqrt(x), sumw2)
if opts.LOGY:
plt.set_yscale("log")
if opts.LOGX:
plt.set_xscale("log")
thislegend = Legend(h.title,h.title,"green")
if opts.LEGEND!="":
leg = readLegend(opts.LEGEND)
for l in leg:
if l.title==h.title:
thislegend=l
#ax.errorbar(centers, sumw, yerr=yerr, xerr=widths, fmt='o')
plt.figure(fig)
plt.hist(centers,bins=len(centers),weights=sumw,normed=opts.NORM,facecolor=thislegend.color,alpha=0.5)
plt.title(thislegend.nicename)
plt.show()
def metal_vs_age_subplot(plt,fits,metal,bv =.65,pdfPage=None,showPlots=False,title=None,\
shadeScatter=False,errorbars=True,bv_m=None,upper_lim=None, \
mamajek_poly=False,metal_val=None,logAge = True,plotStars=False,omit_cluster=None,legend=True):
const = init_constants(metal)
rhk, scatter, CLUSTER_AGES, CLUSTER_NAMES = my_fits.get_valid_metal(
bv, fits, const, None, None, omit_cluster)
#plt.figure(figsize=(7,5))
if metal_val:
plt.hlines(metal_val,0,const.GALAXY_AGE,linestyle='dashed',color='orange',\
label=metal[:2].title() + ' = %.2f' % metal_val)
MARKERS, COLORS = const.MARKERS, const.COLORS
if metal == 'lithium':
if bv >= const.BLDB_LOWER_LIM:
colors = [
const.COLORS[const.CLUSTER_NAMES.index(name)]
for name in CLUSTER_NAMES[1:-1]
]
markers = [
const.MARKERS[const.CLUSTER_NAMES.index(name)]
for name in CLUSTER_NAMES[1:-1]
]
MARKERS = [const.PRIM_MARKER] + markers + [const.BLDB_MARKER]
COLORS = [const.PRIM_COLOR] + colors + [const.BLDB_COLOR]
else:
colors = [
const.COLORS[const.CLUSTER_NAMES.index(name)]
for name in CLUSTER_NAMES[1:]
]
markers = [
const.MARKERS[const.CLUSTER_NAMES.index(name)]
for name in CLUSTER_NAMES[1:]
]
MARKERS = [const.PRIM_MARKER] + markers
COLORS = [const.PRIM_COLOR] + colors
for i in range(len(rhk)):
if (errorbars):
plt.errorbar(CLUSTER_AGES[i], rhk[i], yerr=scatter[i],color=COLORS[i],\
marker=MARKERS[i],markersize=8,capsize=4,zorder=10)
plt.scatter([], [],
color=COLORS[i],
marker=MARKERS[i],
label=CLUSTER_NAMES[i])
else:
plt.scatter(CLUSTER_AGES[i], rhk[i],color=COLORS[i],marker=MARKERS[i],\
label=CLUSTER_NAMES[i],s=80,zorder=10)
mu, sig, lbl = my_fits.vs_age_fits(bv, CLUSTER_AGES, rhk, scatter, metal,
omit_cluster)
plt.plot(const.AGE, mu(const.AGE), label=lbl)
if (plotStars and bv_m is not None):
bv_threshold = 0.05 if metal == 'lithium' else 1
for c in range(len(bv_m)):
bv_arr = np.array(bv_m[c][0])
mask = (bv_arr >= bv - bv_threshold) & (bv_arr <=
bv + bv_threshold)
li = np.array(bv_m[c][1])[mask]
star_ages = [const.CLUSTER_AGES[c]] * len(li)
ul = np.array(
upper_lim[c])[mask] if upper_lim is not None else None
for j in range(len(li)):
m = const.DOWN_ARROW if (upper_lim is not None
and ul[j]) else '+'
color = const.COLORS[
c] # if metal == 'lithium' else const.COLORS[c]
plt.scatter(const.CLUSTER_AGES[c],
li[j],
marker=m,
s=30,
color=color) #for primordial li
if (shadeScatter):
plt.fill_between(const.AGE,
mu(const.AGE) - sig(const.AGE),
mu(const.AGE) + sig(const.AGE),
alpha=.2,
color='C0')
if (mamajek_poly and metal[0].lower() == 'c'):
plt.plot(const.AGE,utils.getMamaRHK(const.AGE),linestyle='--',color='gray',\
label='M & H 2008')
#ax = plt.gca()
if logAge: plt.set_xscale('log')
plt.axis([1, const.GALAXY_AGE] + const.METAL_RANGE)
if legend:
plt.legend()
#plt.set_xlabel('Age (Myr)',size=AXIS_LABEL_SIZE)
#ylabel = set_ylabel(metal)
if (title):
plt.set_title(title, size=TITLE_SIZE)
#plt.tight_layout()
plt.minorticks_on()
plt.tick_params(axis='both', which='both', right=True, top=True)